Modern computing and data architectures have transitioned from local computing and storage schemes to remote and offsite computing and storage via distributed computing systems (e.g., the cloud). While these remote and distributed architectures may typically be useful in implementing traditional computing needs in which content stored at the remote distributed computing systems are distributed to edge devices (e.g., personal computers, mobile devices, etc.), these remote computing and data storage architectures do not function efficiently for edge computing devices that generate very large amounts of data and that require offloading of the very large amounts of data to remote distributed environments such as public or private cloud instances.
That is, in circumstances in which a high-data generating edge device, such as an autonomous vehicle, is routinely generating and/or capturing very large sums of data (e.g., 40 terabytes of data per hour or greater) rather than consuming very large sums of data, storage and processing of the generated data becomes a significant technical problem. Specifically, onsite storage of significant data amounts at an autonomous mobility implementation generated over multiple cycles is unfeasible and transmitting large sums of data from an autonomous mobility edge device to a remote storage scheme (e.g., the cloud) over a communication network may be severely limited by insufficient bandwidth.
In addition, when looking at the deployment of applications generally, an associated cost function typically exists such that the closer the vehicle is in proximity to the cloud infrastructure the application communicates with, the more efficient it becomes to execute a given workflow. The cost of deployment is therefore at its lowest point within the heart of the cloud infrastructure. However, this cost function changes when applications are deployed on autonomous vehicles and for autonomous mobility tasks. In such a context, the applications run on data collected during operation of the autonomous vehicle, which is a significant amount of data collected in a given time. The data rate increase at the edge will therefore outstrip any infrastructure investment considered feasible. The problem in this situation becomes determining how to take the data and applications used for autonomous vehicles and orchestrate them to the right location for the best possible outcome in terms of data offloading and processing to provide an optimally low computational expenditure.
Thus, there is a need in the computing and data storage architecture field to create new and useful systems and methods that seek to alleviate the computing and data storage constraints at autonomous mobility implementations. The below-described embodiments of the present application provide an advanced computing and data storage architecture that addresses at least the above-noted technical problem as well as the problems described expressly or implicitly within the description of the present application.